Results 41 to 50 of about 117,319,651 (289)
BarekengMulti-Output Machine Learning is an advancement of traditional machine learning, designed to predict multiple output variables simultaneously while considering the relationships between these output variables.
Karin Joan, Robyn Irawan, Benny Yong
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A new approach fits multivariate genomic prediction models efficiently
Genetics Selection Evolution, 2022 Background Fast, memory-efficient, and reliable algorithms for estimating genomic estimated breeding values (GEBV) for multiple traits and environments are needed to make timely decisions in breeding.
Alencar Xavier, David Habier
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Projection Pursuit Multivariate Sampling of Parameter Uncertainty
Applied Sciences, 2022 The efficiency of sampling is a critical concern in Monte Carlo analysis, which is frequently used to assess the effect of the uncertainty of the input variables on the uncertainty of the model outputs.
Oktay Erten +2 more
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FEBS Open Bio, EarlyView.NMR metabolomics revealed concentration‐dependent metabolic perturbations in HepG2 cells exposed to H2O2. Rifampicin pretreatment enhanced metabolic competence, attenuated toxin‐induced alterations and produced metabolite profiles more consistent with human liver physiology, supporting the use of CYP450‐induced HepG2 models for improved in vitro ...
Maren Jinks +4 more
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Memory and Resting‐State Connectivity in Acute Transient Global Amnesia: A Case–Control fMRI Study
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Background and Objectives Transient global amnesia (TGA) is a striking model of isolated amnesia. While hippocampal lesions are well described, the network‐level mechanisms and the precise neuropsychological profile remain debated. Our objective was thus to characterize functional and neuropsychological correlates of acute TGA and their ...
Elias El Otmani +10 more
wiley +1 more source
IATSS Research, 2021 Road safety modeling enables the development of crash prediction models and the investigation of which factors contribute to crash occurrence. Developing multivariate response models is also valuable, but such models are currently under-exploited ...
Philippe Barbosa Silva +2 more
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Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective We aim to comprehensively analyze how regional tumor and edema characteristics are associated with clinical presentations and survival outcomes in a large cohort of glioblastoma patients. Methods Patients with IDH‐wildtype glioblastoma who received brain MRI from 2010 to 2023 were included.
Daniel J. Zhou +16 more
wiley +1 more source
Efficient Bayesian Estimation of a Multivariate Stochastic Volatility Model with Cross Leverage and Heavy-Tailed Errors [PDF]
An efficient Bayesian estimation using a Markov chain Monte Carlo method is proposed in the case of a multivariate stochastic volatility model as a natural extension of the univariate stochastic volatility model with leverage and heavy-tailed errors ...
Yasuhiro Omori, Tsunehiro Ishihara
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Generalized least squares estimation of the functional multivariate linear errors-in-variables model
Journal of Multivariate Analysis, 1986 The method of generalized least squares is applied to the sample matrix of mean squares and products to obtain estimators of the parameters of the functional multivariate linear errors-in-variables model. These estimators are shown to be consistent and asymptotically multivariate normal. Relationships between generalized least squares estimation of the
Dahm, P.Fred, Fuller, Wayne A
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Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Facioscapulohumeral muscular dystrophy (FSHD) is one of the most debilitating and common muscular dystrophies. Despite its severity, no approved therapy exists for FSHD patients. However, several therapeutic candidates are currently under development, and some have recently entered clinical trials, marking the need for reliable ...
Mustafa Bilal Bayazit +11 more
wiley +1 more source